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Learning from Data: A Short Course
A comprehensive introductory textbook that teaches statistical reasoning as a way of learning about the world from variable, messy data through descriptive and inferential procedures.
A profile of this book is on the way.
What it’s about
Learning From Data: An Introduction to Statistical Reasoning teaches readers a new way of thinking about and learning from data across psychological, social, educational, political, and economic domains. Rather than treating statistics as mere number-crunching, the book emphasizes the logic underlying each procedure: why variability matters, how sampling distributions enable inference, and how the six-step hypothesis-testing schema applies from the simplest z-test to complex factorial ANOVA. Built around two real datasets (a smoking-cessation study and a maternity/marital-satisfaction study), the book devotes a full chapter to each difficult concept, uses extensive repetition, and integrates parametric and nonparametric procedures so students learn to choose the right tool. It uniquely confronts the gap between random sampling (which statistics textbooks preach) and random assignment (which experiments actually use), giving readers the conceptual tools to question and challenge data-based claims and to conduct sound research themselves.
The through-line
- Who it’s for
- A student or researcher in the behavioral sciences who wants to understand, conduct, and critically evaluate data-based claims about the world.
- The problem
- Data in the behavioral sciences are messy and variable, making it impossible to see clear facts without proper analysis, and the reader must choose and apply the right statistical procedure. The reader feels intimidated by statistics, fears the math, and worries they cannot tell good data from misleading claims.
- The plan
- Learn to describe data with frequency distributions, central tendency, variability, and z scores.
- Understand probability and sampling distributions as the foundation of inference.
- Master the six-step hypothesis-testing schema and apply it across procedures.
- Use the Statistical Selection Guide to pick the right test for any situation.
- Distinguish random sampling from random assignment to know what you can conclude.
- The payoff
- The reader can choose and correctly apply the appropriate statistical procedure for any situation. · The reader can interpret data and their limitations, drawing conclusions only about the populations sampled. · The reader can critically question and challenge data-based claims in everyday life.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.